Evaluation Metric. We choose the
Inception Score (IS)  [1]}, Fréchet Inception Distance (FID) [2]}, and R-precisions [3]} as the quantitive metrics to evaluate the performance.
The IS calculates the KL-divergence between the conditional and marginal probability distributions.
In general, a larger IS indicates the generative model can synthesize fake images with better diversity and quality.
The FID computes the Fréchet distance between synthetic and real images in the feature space extracted from the pre-trained Inception v3 model. A smaller FID indicates the synthetic data is more realistic and similar to the true data. R-precision calculates the precision of the image-text retrieval task to evaluate to what extent the synthetic images match the input captions. The higher R-precision indicates the generated images have greater consistency with the text descriptions. After training, the model generates 30,000 images conditioned on the captions in the test set for evaluation. The source code to calculate the three metrics is from the public websitehttps://github.com/MinfengZhu/DM-GAN.
